PartialAutoCorrelationTransformer
Partial auto-correlation transformer.
The partial autocorrelation function measures the conditional correlation between a timeseries and its self at different lags. In particular, the correlation between a time period and a lag, is calculated conditional on all the points between the time period and the lag.
The PartialAutoCorrelationTransformer returns these values as a series for each lag up to the n_lags specified.
Quickstart
from sktime.transformations.acf import PartialAutoCorrelationTransformer
estimator = PartialAutoCorrelationTransformer(n_lags=None, method='ywadjusted')Tags
Capabilities
- Unequal-length series
- Multivariate: Not supported
- Inverse transform: Not supported
- Missing values: Not supported
- Removes missing values: Not supported
- Equalizes series length: Not supported
Properties
- Input typescitype:transform-input
- Series
- Output typescitype:transform-output
- Series
- Label typescitype:transform-labels
- None
- Fit is emptyfit_is_empty
- Yes
- Keeps the time indextransform-returns-same-time-index
- No
- Requires Xrequires_X
- Yes
- Requires yrequires_y
- No
- X and y need the same indexX-y-must-have-same-index
- No
Parameters(2)
- n_lagsint, default=None
- Number of lags to return partial autocorrelation for. If None, statsmodels acf function uses min(10 * np.log10(nobs), nobs // 2 - 1).
- methodstr, default=”ywadjusted”
Specifies which method for the calculations to use.
“yw” or “ywadjusted”: Yule-Walker with sample-size adjustment in denominator for acovf. Default.
“ywm” or “ywmle”: Yule-Walker without adjustment.
“ols”: regression of time series on lags of it and on constant.
“ols-inefficient”: regression of time series on lags using a single common sample to estimate all pacf coefficients.
“ols-adjusted”: regression of time series on lags with a bias adjustment.
“ld” or “ldadjusted”: Levinson-Durbin recursion with bias correction.
“ldb” or “ldbiased”: Levinson-Durbin recursion without bias correction.
Examples
>>> from sktime.transformations.acf import PartialAutoCorrelationTransformer
>>> from sktime.datasets import load_airline
>>> y = load_airline ()
>>> transformer = PartialAutoCorrelationTransformer (n_lags = 12)
>>> y_hat = transformer. fit_transform (y)